Mathematics

Research conducted at University of Leuven has updated our knowledge about statistics

  2008 NOV 3 - (VerticalNews.com) -- "Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this," scientists writing in the journal Statistical Science report.

  "First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the complete-data situation and some of their properties vanish when they are fitted to incomplete, unbalanced data. These and other issues are brought forward using two key examples, one of a continuous and one of a categorical nature," wrote G. Verbeke and colleagues, University of Leuven.

  The researchers concluded: "We argue that model assessment ought to consist of two parts: (i) assessment of a model's fit to the observed data and (ii) assessment of the sensitivity of inferences to unverifiable assumptions, that is, to how a model described the unobserved data given the observed ones."

  Verbeke and colleagues published their study in Statistical Science (Formal and informal model selection with incomplete data. Statistical Science, 2008;23(2):201-218).

  Additional information can be obtained by contacting G. Verbeke, Catholic University of Leuven, Center Biostatistics, Kapucijnenvoer 35, B-3000 Louvain, Belgium.

  The publisher of the journal Statistical Science can be contacted at: Institute Mathematical Statistics, PO Box 22718, Beachwood, OH 44122, USA.

  Keywords: Statistics, University of Leuven.

  This article was prepared by VerticalNews Mathematics editors from staff and other reports. Copyright 2008, VerticalNews Mathematics via VerticalNews.com.